Conversion rate is a very important metric, used properly. Here is my point of view on the basics and best practices for measuring conversion rate.

Definition first:Conversion rate, in percentage, equals Outcomes divided by Unique Visitors during a particular time period.

What are Outcomes: From a macro perspective any reason for which your website exists. Most frequently this is the total number of orders submitted or total number of leads collected or total number of newsletter/email sign-ups. For non-ecommerce websites it can be number of people who completed a task, so for a support site the number of people who got to a faq or a answer or a knowledge base article (this is a really crude measure of success for a support site but it is something).

Why use Unique Visitors: There is a lot of heat around this topic. Some people are in the Total Visitors camp and others are in the Unique Visitors camp. Remember trends are important so if you stick to whatever you prefer just be consistent over time and you’ll be fine (don’t feel the need to get “un-brainwashed” from whatever camp you are currently in : )).

My personal view on why we should use Unique is that every session is not an opportunity to get a customer to hit submit order (I do realize this sounds scandalous, more so because like a majority of you I live in the world of ecommerce : ).

Shopping / web surfing is a delicate dance of come to see who we (the company) are, go to another site read reviews, then come to see our benefits, then to ask you wife it is ok for you to buy and then come purchase. I am probably missing a few other steps in there.

Using Unique Visitors is a better read of what is really happening on your website because it accommodates this dance and gives you "credit" for those prior sessions when the dance was on. More importantly being a practitioner I feel metric definitions should incorporate on the ground reality and using Unique Visitors accommodates that reality. (Matt Belkin, you have read his interview on this blog, has a great alternative point of view on this, click here for that.)

Uniqueness is currently measured by setting a persistent cookie (call it shopper_id) most of the time and they are a bit unreliable (I have to stress that certainly not as much as the hoopla that surrounds them) and hence this is not optimal. But still the best we got. If you disagree please recall the statement above about consistency and trends.

Time period: If you are measuring Weekly Conversion Rate it is the sum of orders during that time and sum of unique shopper_id's during the same weekly time period. It is not the sum of unique visitors on each day and then a sum of that daily unique visitor number for a week. For monthly sum of orders during that month and sum of unique shopper_id's during that time. It is not recommended to sum daily unique visitors to get a total for month or week.

All of the above might seem to be too much detail just to get started on the metric but all this detail reflects the importance placed on this metric and how most of the time we don’t even agree on what the definition is and how it should be measured. So hopefully it was helpful to you.

Ok so here seven simple best practices related to conversion rate:

# 7 : Start with overall site conversion rate. (Then promptly forget about it.) There is no other metric that will tell you less about your website than overall conversion rate (assuming you have more than $ 25,000 in sales on your website) but overall conversion rate is easy to measure so you should do it, I recommend using the definition above, and get it out of your system.

# 6 : Trend over time and don’t forget seasonality. If you have read this blog for any amount of time you know I love two things: trends and segmentation (more on this later). Most definitely trend conversion rate numbers but what is unique about this metric is that more than others it is really impacted by seasonality and so do things like 13 month trends or look at 5 quarters or 8 days.

In each case you will have same period from history to compare with. That will give you a lot more context and avoid sub optimal reactions / actions by comparing time periods that have nothing do to with each other, say last month vs this (unless your “season” is that, which it could be).

# 5 : Understand exactly what the acquisition strategy of your website / company is. This is not a report, it is a conversation / investigation with your business partners and it is an extremely important step that any analyst needs complete. Figure out what is your core acquisition strategy and then measure conversion rate for those elements. Is your company heavily into Direct Marketing (email, snail mail etc)? Are you spending excessively on PPC (Pay Per Click)? Or maybe you are about to plunk down a million dollars on a new affiliate marketing strategy or maybe on SEO.

Do this before you get too deep into conversion rate measurement because it will without a doubt lead to more meaningful analysis from you and, hyper importantly, you will measure what is important to your company rather than what your friendly neighborhood web analytics tool is throwing up at you.

# 4 :Conversion rate by top five referring url's. This sounds really simple and silly but there is usually a disconnect between what the company strategy is and where the traffic really comes from.

So for example if you are a complex business with many websites you might not necessarily be measuring conversion rate for your corporate site which might be sending you tons of traffic. Or from some blog that started to praise you a lot. You get the idea. I always find “hidden gems” in the referring urls and measuring conversion for your top five referring url's is a great insights finding insurance policy.

If you find that this report shows you the same stuff as some of the above or below you can stop it. But if you are not doing this I bet you are missing something delightful.

# 3 : Don’t measure conversion rate by page or link. This is a request we all get and often execute on. It is rather sub optimal. In a complex multi page and multi link web environment how could you possibly measure “conversion rate by page”? Unless your web scenario is: Two people come to the site, one enters at the home page and the other at product overview page and you can get to checkout directly from both pages measuring conversion rate “of” each page is very misleading.

In the click density (site overlay) report some web analytics tools show conversion rate for each link on the page. The hypothesis is that x % of people who clicked on this link purchased. Unless all these links lead to the checkout this is a useless piece of information.

In a multi page complex path web experience simply the fact that someone saw a page or clicked on a link is not enough to attribute any credit that page / link in terms of conversion rate. (If you want to measure value of a page see the approach described in this post and look for the section where we talk about “page influence”.)

# 2 : Segment like crazy. Most websites convert in single digits (usually low single digits). With such a small ratio of people converting the “insights gold” is hidden deep in your site data and it is extremely critical that you segment “like crazy” in order to find your insights. This means showing top 5 (or x where you pick the x right for your company) segments of conversion rate, we have discussed two above already, referring urls and core acquisition strategies (DM, PPC, SEO, Affiliates etc).

That is just a start. You should have indented sub segments for each of the top five. Something like conversion rate of top five sub segments for each segment of conversion report so you can really show where the desired outcomes are coming from. So top five direct marketing campaigns, top five PPC key phrases driving conversion or top five affiliates etc. This is not too complex for any top website all this information will fit on one page (and with 10 size font : ).

# 1 : Always show revenue next to conversion rate. Another extremely simple trick to provide context. We usually create a report that will show various conversion rate buckets (like the idea in #6 above). But conversion rate just by itself can be misleading in terms of opportunity for any website. So my recommendation is to show the actual revenue number (or leads or newsletter sign ups or whatever is your conversion) next to the conversion rate %.

What you will find is that some of your highest conversion rates don’t bring in most of the revenue, it could the line in your report with the fifth highest conversion rate (or whatever).

Typically people see a high conversion rate and think “let’s do more of that”. But the highest conversion rate could come when you give deep discounts (which you can’t do all the time) or come from segments of customers you can’t find more of (say existing customers). Providing outcome numbers next to the % gives more meaningful data to your decision makers. Sounds simple but is very effective.

One last bonus recommendation for our dear blog readers……

# 0 : Never measure conversion rate without a goal. This is not always possible but a highly recommended best practice. Having a goal gives context to your actual number, asking your business decision makers forces them to think about where the revenue (or other outcomes) will come from causing them to really analyze their execution strategies and try to plan them ahead of time as much as possible.

Having a goal guides the conversation and analysis and the deep dives that will yield insights and asking for a goal makes you integrated with the business decision making process (because almost always the user of the web analytics tools is not the one that will set goals).

Like best practice #5 above this recommendation is not so much just to have on the report, which is important, but to insert a social / cultural change in your business. In Web Analytics goals are hard to find and yet there is enormous pressure from your business leads for insights and action recommendation. Ask for a goal, force them to think and create a environment where you, dear analysts, lay some responsibility for business planning and execution on their door. In the end you will look like a hero if you help push this change in the typical company culture.

I do realize the irony that all of the recommendations above might, just might, end up getting you to obsess about conversion rate more than you should. : ) Do please avoid that tendency and see the post on why you should not obsess about conversion rate. : )

Agree with the best practices recommended above? Are there tips and tricks that have worked for you better? Please share your thoughts, critique, feedback, random musings with us via comments. : )

This was an excellent post and I have my work cut out for me (as I need to take some of it and apply it to my clients).

A couple of extra tools/tips. For the part with sesional conversion rates – I have successfully co-related keyword seasonality demand (from Google AdWords Keyword Research tool) to traffic and revenue to a client (conversion rate) by super imposing the two patterns.

I like showing revenue next to conversion rate – I haven't done it often or consistantly (except in a scorecard format) but would like to now.

And segmenting like crazy….who can ever do enough of that? Thanks for a great Post!

Very insightful post Avinash (or should that be inciteful?). As usual your unique perspective gives us all lots to think about. I do wonder about your definition at the beginning though. You don't distinguish between order and buyer conversion rates. My understanding is that they differ in the denominator they use (one uses visits while the other uses visitors). Can I assume you prefer buyer conversion rate since you used visitors in your definition?

Jamie: Thanks for replying to my email and clarifying the definitions of the two metrics you mention in your comment:

Order conversion rate = total number of orders taken / total visits
Buyer conversion rate = total customers converted / all visitors

Your question was:

Can I assume you prefer buyer conversion rate since you used visitors in your definition?

The definition recommended in the post above is neither of the two you mention. This is definition recommended my post:

Site Conversion Rate = Total Number of Outcomes / Total Unique Visitors during a time period

My personal point of view on the two you shared:

Order Conversion Rate: This is a bit sub optimal, for most business on the web I would stress test the rationale for using "visits". Please see the section titled "Why Use Unique Visitors" in the post above. Using "visits" might yield sub optimal insights. IMHO. YMMV.

Buyer Conversion Rate: This is a bit more interesting as you have defined it. I am struggling though to imagine what it is telling me and what actions I could take if it were moving up or down (and overlay that with orders going up or down). The only scenario where this might be meaningful is if a website gets lots of orders from repeat buyers, same people buying a lot. Taking orders ("outcomes") out of the equation might provide some insight. If you use this metric be careful about how you count "customers". It is Avinash Kaushik or everyone in Avinash's Home/Office or someone who has never purchased from you or how do you know someone is a "customer". Sans the answer to this you are back to orders. I have this "actionability test" for a metric and my tiny brain is struggling with getting this metric to pass that test. Again: IMHO. YMMV.

I think Site Conversion Rate (which was not invented by me/us but is a fairly well established standard) accommodates for both of new metrics you mention and does it is a way that is clean and will provide insights (of course if you follow the eight best practices! : )).

I want to hasten and clarify that each business should review their business model and customer interactions and then decide on what is the best metric definition for them. You should certainly not go with what some random blogger, me, says in a blog post. You in your company are best at deciding what is best for you. Though outside points of view can be helpful and informational, I would not recommend copy pasting metrics in.

I enjoyed reading avinashs posting and the comments. You guys seem to be very knowledgable. therefore I thought you maight have some answers for me. I am looking for some statistical data (I can't find this anywhere on the web…):

1. What is the typical average number of visitors of a new webpage (given you have good marketing to get people on your webpage)?

2. What is a typical conversion rate for visitors of a webpage that sells a membership to have access to premium services/ content etc.?

Gandalf: I'll go out on a limb and say that you won't find the data you are looking for. One main reason: the questions are too broad and the web is too complex and niche.

1. What is the typical average number of visitors of a new webpage?

On this blog, 200. On oracle.com 500k. On cnn.com 1 million. And so on and so forth. Most companies won't share their data publicly. Two recommendations:
1) Benchmark against yourself and get better over time.
2) Get access to a Competitive Analysis service (Click Here for a post on options).

2. What is a typical conversion rate for visitors of a webpage that sells a membership to have access to premium services/ content etc.?

Best bet would be accessing a service such a Jupiter or Forrester and see if they have a "vertical" report (vertical = your business segment).

3. Any data on how to increase conversion rates/ tips and tricks

You have read this article already, I hope it was of some help. :) I would also recommend testing as perhaps the most powerful tool in improving conversion of any sort.

Click Here for a primer on testing. Click Herefor a post on the cultural elements of a successful testing program.

I have a goal which is to measure the number of visitors who sign up for a newsletter. I have set a thank you page as the goal. It is the page shown to user when they finish subscribing for the newsletter.

What i find confusing is that my goal conversion, page views of thank you page as well as my database records of subscribers all show different figures even though i have selected the whole range.

Kirsby: There could be many reasons for this. Some visitors might not have javascript turned on so for them page views are not recorded (decreasing the count). It could be that people see the thank you page and then browse and hit the back button and load the page again (increase the count). And some other people might have other behavior that could cause the number to be different.

So in a nutshell, the numbers will never tie. But they should be close (say between 5 – 10%), if not I would dig deeper and try to understand more. It might need a bit more knowledge of the technology, but you should be able to figure out the root causes (just don't expect them to match completely!).

Avinash: Thanks a lot for your thought on this. I am quite surprise myself by the difference. The number of registration in my database is around 5k plus. The page views on the goal page is closer at 5k and my goal conversion is totally off at 3k plus.

I think this brought up an important point mention in the book on data collection. I could have easily get the more reliable data from my database rather than simply stick to GA for sake of convenience.

NUMBER OF VISITORS IS DECREASING!
Hi. I have a concern about the number of visitors on my page.
e.g: yesterday I had 240 visitors, today I have 210??????????????
Please, help me figure out why is this happening.

How would you measure different sources/mediums without conversion goals?

Unfortunately we've yet to tag our 'dynamic' pages (they all appear as the same URL/title) – something on the backburner for IT, but crucial for us in marketing, so we don't have access to conversion goals yet (we're running GA).

A really good read.
A little confused on the statement that unique visits includes people who have left and come back? Surely that isn't correct? Wouldn't total visits cater for this criteria?

Something else which wasn't mentioned, but I don't know how relevant it is and whether people care, but I take into consideration the use of roaming IP ranges. A unique visit could be the same person 20 times at 20 different points in time serving 20 different IP addresses from their ISP.

John: If the conversion point is one step removed, I think you can read a lot into the "landing page conversion".

But if conversion is several pages away, as is typical, conversion is not a good indicator because the subsequent pages have so much more influence weather conversions happen or not.

The more the number of pages the worse your data.

By setting up the landing page to convert (in a situation where there are many pages in between the landing page and the goal) you are placing an overwhelming burden where it might not belong (and there is a small chance you'll focus too much on the landing page rather than subsequent broken pages).

Here's my alternative: Measure the bounce rate of landing pages.

The job of landing pages is to engage people who land and stop them from leaving instantly. If the landing page gets the person to click on the right call to action it has done its job, now its up to rest of the site.

If Google Analytics for instance allows visitors to convert a second time or third time on follow up visits, and unique visitors is used to calculate conversion rate, isn't it true that converion rate could exceed 100%? Would this not cause the data to be skewed if one visitor converted a bunch of times. It might give you a false sense of success or am I missing something?

Levi: You are right, Google Analytics, and all other web analytics tools, will behave that way. If the same person comes again and again and buys things from you each of those are counted as conversions. Because they are conversions. No?

If your business involves that happening then certainly you should be careful of this phenomenon. One alternative is to add a metric to give context, like Unique Orders Per Customer (quite easy to do in any tool with a custom report or through the API). You can also dedupe the orders by unique customer and compute conversions.

While on paper it is possible conversion rate could exceed 100%, it is rare that this happens in real life. So most of the time this kind of exercises are good for mental stimulation, but of minor, if any, practical value.

Hello, since the last couple of days, for some reason my Thank You page is showing in the landing pages in GA, and is impacting dramatically one of my goals.
I can force the funnel by making the previous step mandatory, so the funnel will only record the pages that came from the previous page (and therefore not the ones where the Goal page came directly from a search result), however according to the GA help, this will not impact the Goal itself, which will continue to record all visits.

I really enjoyed the " best practices for conversion". To find out your top 5 referring URLs by segmenting. What would the best tool to back track the shopping cart results to referring sites in an e-commerce site ?

Matt: If your shopping cart is on your site's domain then you can simply use a tool like SiteCatalyst, WebTrends, Google Analytics etc. It is trivial to segment people who during their visit added a product to cart and then see what the referring sources are.

If your shopping cart is on a different domain you can work with a consultant to implement cross domain tracking. It is possible to do this for many shopping cart providers, though not all. If you use GA then here's a list of consultants: http://bit.ly/gaac

I feel like you very rarely give more than a passing hand wave to conversion outcomes that could be measured for non-ecommerce sites. Would you mind offering some counsel for a content site like a lyrics site in regards to outcomes that could be measured?

Regardless of the type of site it all comes down to your ability to identify your macro and micro conversions, and then (with extra effort) compute the economic value. I'm calling them conversions, but they are better thought of as "business outcomes."

If you can't identify any macro or micro business outcomes, the chances of using data to drive decisions are dramatically minimized.

Check out this post, it outlines (using non-ecommerce and non-profit examples) how to go about this process:

The micro business outcomes could be: % of lyrics pages viewed, number of lyrics corrections, % of lyrics with comments, social clicks, # of songs played, reviews read/submitted, # of ads viewed per visit, visitor loyalty (say greater than 10 visits a month) etc. etc.

Once you have this you can prioritize what's important, then you measure, then you understand what marketing activity is causing your prioritized business outcomes, then you do more or less of what makes sense.

Testing platforms will measure the total outcomes (whatever metric you use) when the test is running and declare success of victory when statistical significance is achieved. So in that sense you are not measuring conversions every day to check A is better than B, because the results may or may not be statistically significant.

I was wondering if it is possible to something like this: Find out unique visitors who perform multiple actions (Say, unique visitors who visit pages A and B; Or unique visitors who click on both links X and Y?)

The reason I need to do this is – In a gamified system, there is a user goal that is considered complete when two sub tasks are completed (Two links are clicked). So we need unique users who have clicked on both links (i.e. intersection of unique clickers on the two links)?

I may have missed this in the product documentation or may not have searched smartly, but could not find a way to do this. Would appreciate your thoughts.

Maybe I'm not grasping something yet — in point #0, I envision companies setting goals like, "i want to increase conversion rate by X%, which is projected to increase revenue by X$", which is something you cautioned us against doing.

Can you give an example of a goal that you are referring to in point #0?

I liked your tip about including the absolute $ amount / number of leads along with the CR % to add context. Small comment and Q – In addition to including the $ amount from lets say, top 5 URLs or DM Campaigns, if the total revenue for the site (and % of top 5 to total) can also be included in the same CR report, it could help further understand if the top 5 lines are statistically significant or that further investigation is required to check say, top 10 lines before going ahead with a deep dive.

For example, if you have 1000 visitors to your website, and 20 of them bought something, you have a conversion ratio of 0.02 or 2%.

There are lots of nuances when you measure these numbers which is an article of its own about web analytics (this is a good read), but important thing is to have some measure and keep tracking this number.

This is a fantastic article and all the comments are a real testament to the effect and thought you continue to evoke in the industry. Thank you. I've searched the web for this question and would love your input. As far as goals for funnel tests, a coworker and I are wrestling over some key items in our test planning. It's a eCommerce site that has a pretty straight forward multi-step checkout with no navigation or additional upsells during that process. It's pretty standard: login>address>shipping>billing>confirmation>Order Confirmed. If we did a test on any of these pages, do you think:

a) Do you think the primary goal should be conversion rate (and not revenue) since a user has a heavy propensity to buy (70% completion rate from login- to order).

b) If it is conversion rate, is that primary goal, conversion rate to the next step or overall?

c) In computing an estimate of site traffic needed for our test, should we use the conversion rates noted above, in lieu of the site conversion rate. (again since this segment of users is so very different).

A) If you are testing one of the pages, you can measure the overall conversion rate (even with the propensity to buy – that would not change across test participant clusters). But better still is to juts measure how good each version is in getting people to not abandon that stage (i.e. how successful it is in getting people to the next stage). That, after all, is its job.

B) Getting to the next stage is primary, the conversion rate would be secondary (if your testing tool allows you to measure both).

C) You will need to compute test participants, which in your case would be the number of people hitting the start checkout button. Where site traffic can play a role is to check how many you need to go from site visit started to start checkout. (If you use Google Analytics' Enhanced Ecommerce, this is a standard computation there).

I must admit I found the experience of reading the article akin to the ones I used to have in school when attending mathematics class. No offence :p

But the point that I'd like to make is, while uniques give an idea of how many new people you are reaching (preferably through organic search), don't returning visitors indicate a measure of visitor / customer satisfaction and the value you provide?

If you do not have a substantial number of returning visitors coming to your site from their own bookmarks or whatever methods they use to keep track, are you really providing any significant value? And isn't that going to affect your conversion in any case?

Awesome and helpful post, I have learned over the years that integrating great high-res images an videos (if possible), boost conversion rate as well – making the images and videos shareable with a simple click of a button, boosts the figure even higher.

Trackbacks

One thing about Avinash Kaushik – he writes great posts in his blog Occam's Razor. I feel that Avinash has actually done it this time…he's written "gold" – gave out something that no one has ever said before about conversion……

[…] 1) Avinash Kaushik at his blog and published work suggests to use an always referenced conversion rate but take careful look at what info it might provide. Even a slight move by a point in the conversion rate might translate into millions of profit for a business. At the same time, obsessing with this metric might become a short-term focused strategy that takes away from the quality of a user experience. Moreover, it also focuses only on small portion of the site visitors, that might not be even interested in all that content and interactivity. What about the rest…that stumble upon purposefully or not? You lose them. Thus, he advises to use an alternative metric: "task completion rate by primary purpose". Thus, you start driving your efforts to develop a site that helps all potential users/visitors to accomplish their "missions" . […]

[…]
So a little while ago, I picked up Web Analytics: An Hour a Day by Avinash Kaushik. It’s a nice book that gives practical advice on what kind of things you can measure with different analytics packages, and more importantly, what they mean for your business. Check it out if you love your analytics stats, but you don’t feel like you’re using them to your maximum potential.

Avinash blogs over at Occam’s Razor, and in our research for MailChimp’s Analytics360 tool, I stumbled across: Excellent Analytics Tip#5: Conversion Rate Basics & Best Practices

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Let’s pick apart three powerful conversion rates that I have been using web analytics to do deep dives with one of my customers.

Avinish Kaushik, years ago, shared his views on how to consider conversion rates and what not to do. From his position these metrics may be too granular, but for my customer and me they are invaluable in learning more about how the website is used and what works for the visitors.
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Well, as we’ve already mentioned, web analytics is all about data. And now that you’ve got your conversion rate practices refined, you can start thinking about how you wield that data. Using this data, you can refine the processes on your websites, particularly when it comes to leads and the sales process. Consider that you check your data, and you find that an odd amount of people start the checkout process, but never finish (abandon the checkout). This could be a problem with your security loading speed, trust, your density of text, or even your safe words display being unreadable so they can’t checkout. Once someone has made the decision to buy, they’re close, but not roped in yet, so you need to make the checkout process as smooth as possible, and your analytics data should provide plenty of opportunities and insights towards improvement.
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I like the method Avinash Kaushik proposed for conversion rate at his Occam’s Razor site. Here is the gist of it, of all the unique visitors to the site, how many clicked the link for more information. Some will argue to use pageviews – but I cannot tell a pageview about the book – only a person. You could probably argue the other side successfully. So just be consistent with whatever method you use.
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You can also switch from “Visit-level” to “Visitor-level” on the fly, which can also be useful depending on how your view your business. Some people like to think about every visit being an opportunity to convert on-site, whereas Avinash advocates in his Web Analytics 2.0 book that using Visitors as the denominator for conversion rate is the proper thought model. I won’t weigh in on the difference in this post, but it’s cool that we can now change back-and-forth to see what the differences in the data are.
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Although there is quite some discussion about using Visits or Unique Visitors in the Conversion Rate’s formular, I stick to the latter. I do not believe every Visit is a real, equally weighted chance to hit a Conversion if it is the same person. Depending on the business behind, a conversion may take several Visits (e.g. reading a product information in the morning and buying it in the evening). You can find further information about this idea here (Visit) and here (Unique Visitors).
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For example, if you have 1000 visitors to your website, and 20 of them bought something, you have a conversion ratio of 0.02 or 2%. There are lots of nuances when you measure these numbers which is an article of its own about web analytics (this is a good read), but important thing is to have some measure and keep tracking this number.
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This means that you need to find a way to get past the lead and sale mentality and find ways to engage and develop relationships with the users that found you by accident as well as those who aren’t ready to by or are just looking. Avinash Kaushik post, “Stop Obsessing About Conversion Rate” and it’s follow up post “Excellent Analytics Tip#5: Conversion Rate Basics & Best Practices” are two great articles that follow up on this topic.
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Web Analysts have a lot of choice in most tools about how to segment users. So what should you choose to calculate your OCR%? Avinash makes a good case for using Unique Visitors. That’s attractively simple, but I don’t feel it’s a good fit for all situations. I prefer to choose a metric based on the nature of your traffic and the goals you have set on your website.
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Some websites describe conversion rate as the percentage of visitors who take a desired action (download a free sample document, leave reviews, likes, leave a comment on a blog, and etc.), some websites deem that it is the percentage of visitors who become customers. Here, Avinash Kaushik gives a more specific definition: “Conversion rate, in percentage, equals Outcomes divided by Unique Visitors during a particular time period.” But I think you should consider all.
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